D

Datalayer Jupyter MCP Server

Jupyter MCP Server is a server that implements the Model Context Protocol, supporting interaction with notebooks running on local JupyterLab.
2 points
18

What is Jupyter MCP Server?

Jupyter MCP Server is a server implementation based on the Model Context Protocol (MCP), allowing users to interact with locally running JupyterLab notebooks through the protocol. It provides functions such as adding and executing code cells, adding Markdown cells, enabling AI assistants to directly operate on Jupyter notebooks.

How to use Jupyter MCP Server?

To use Jupyter MCP Server, you need to start the local JupyterLab service first, and then run the MCP server through a Docker container. After configuration, AI assistants such as Claude Desktop can interact with Jupyter notebooks through the MCP protocol.

Applicable scenarios

Suitable for scenarios where AI assistants need to directly operate on Jupyter notebooks, such as automated data analysis, teaching demonstrations, collaborative programming, etc. It is especially suitable for data scientists and developers to enhance their workflows with AI.

Main features

Add and execute code cellsCan add new code cells to the Jupyter notebook and immediately execute the code in them
Add Markdown cellsCan add cells containing Markdown-formatted text to the Jupyter notebook
JupyterLab integrationSeamlessly integrates with local JupyterLab and supports real-time collaboration (RTC) functionality

Advantages and limitations

Advantages
Simplifies the interaction process between AI and Jupyter notebooks
Supports real-time viewing of notebook changes
Lightweight Docker container deployment
Compatible with mainstream AI assistants such as Claude
Limitations
Requires a locally running JupyterLab service
Currently, the functions are relatively basic and only support adding cells
Requires configuration of network access permissions

How to use

Install JupyterLab
Ensure that JupyterLab and related extensions are installed
Start JupyterLab
Run JupyterLab in the terminal, paying attention to setting the token and IP
Configure the AI client
Add the MCP server configuration in clients such as Claude Desktop
Run the MCP server
Run the MCP server container through Docker

Usage examples

Automated data analysisThe AI assistant automatically adds code cells to perform data analysis tasks
Teaching document generationThe AI assistant adds explanatory Markdown documentation for the code

Frequently Asked Questions

Why can't the MCP server connect to my JupyterLab?
How to specify the notebook file to operate on?
Which AI clients are supported?

Related resources

Model Context Protocol official website
Official documentation for the MCP protocol
GitHub repository
Project source code and issue tracking
Smithery installation
One-click installation through Smithery
Installation
Copy the following command to your Client for configuration
{
  "mcpServers": {
    "jupyter": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SERVER_URL",
        "-e",
        "TOKEN",
        "-e",
        "NOTEBOOK_PATH",
        "datalayer/jupyter-mcp-server:latest"
      ],
      "env": {
        "SERVER_URL": "http://host.docker.internal:8888",
        "TOKEN": "MY_TOKEN",
        "NOTEBOOK_PATH": "notebook.ipynb"
      }
    }
  }
}

{
  "mcpServers": {
    "jupyter": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "SERVER_URL",
        "-e",
        "TOKEN",
        "-e",
        "NOTEBOOK_PATH",
        "--network=host",
        "datalayer/jupyter-mcp-server:latest"
      ],
      "env": {
        "SERVER_URL": "http://localhost:8888",
        "TOKEN": "MY_TOKEN",
        "NOTEBOOK_PATH": "notebook.ipynb"
      }
    }
  }
}
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